Methods and systems for computer aided targeting

ABSTRACT

A method for acquiring an image on an imaging system includes accessing at least first image data from a first imaging system, processing the first image data in accordance with a CAD algorithm, acquiring at least second image data based upon results of the CAD algorithm and processing the second image data in accordance with the CAD algorithm to confirm the results of the CAD algorithm regarding the first image data.

BACKGROUND OF THE INVENTION

This invention relates generally to imaging procedures, and moreparticularly to methods and apparatus for improving computer aideddetection or diagnosis by utilizing a computer aided processingtechnique.

Computer aided diagnosis (CAD), such as screening mammography andevaluation of other disease states or medical or physiological events,is typically based upon various types of analysis of a series ofcollected images. The collected images are analyzed by utilizing thepathologies that are highlighted by a CAD algorithm. The results aregenerally viewed by radiologists for final diagnosis. As can beappreciated by those skilled in the art, certain subsequent imagingprocedures may become feasible or may be recognized as desirable due tothe improved management of data volume.

It should be noted that CAD may be utilized in any imaging modality,such as computed tomography (CT), magnetic resonance imaging (MRI),X-ray systems, ultrasound systems, positron emission tomography (PET),and so forth. CAD algorithms in certain of these modalities may provideadvantages over those in other modalities, depending upon the imagingcapabilities of the modality, the tissue being imaged, and so forth.Computed tomography, for example, is generally a diagnostic procedure inwhich cross-sectional images or slices are made by an X-ray system. TheCT scanning procedure combines the use of a computer system and arotating X-ray device to create detailed cross sectional images or“slices” of a patient's organs and other body parts. The imagingcapabilities are physically similar to those of X-ray systems. MRI,ultrasound, PET, and other modalities similarly are adapted to imagingcertain tissues or anatomies, and provide advantages for the differentCAD algorithm employed with images they produce.

Each imaging modality is based upon unique physics and image processingtechniques. For example, a CT system measures the attenuation of X-raybeams passed through a patient from numerous angles, and then, basedupon these measurements, a computer is able to reconstruct images of theportions of a patient's body responsible for the radiation attenuation.As will be appreciated by those skilled in the art, these images arebased upon separate examination of a series of continuous crosssections. Thus, a virtual 3-D image may be produced by a CT examination.It should be pointed out that a CT system does not actually directlyprovide an image, but rather numerical values of tissue density. Theimage based upon the reconstructed data is typically displayed on acathode ray tube, and may be printed or reproduced on film.

Continuing with the example of CT imaging, CT scanners operate byprojecting fan shaped X-ray beams from an X-ray source that iscollimated and passes through the object, such as a patient, that isthen detected by a detector element. The data is then used to produce auseful image. Thus, the detector element produces data based on theattenuation of the X-ray beams, and the data are processed by computeranalysis. The locations of pathologies may then be highlighted by theCAD algorithm, and thus brought to a human observer's attention. Aradiologist or other physician for final diagnosis may then review theresults.

Each imaging modality may provide unique advantages over othermodalities for certain types of disease or physiological conditiondetection. For example, CT scanning provides advantages over other typesof techniques in diagnosing disease particularly because it illustratesthe shape and exact location of organs, soft tissues, and bones for anyslice of the body. Further, CT scans may help doctors distinguishbetween a simple cyst, for example, and a solid tumor, and thus evaluateabnormalities more accurately. As mentioned above, other imagingmodalities are similarly best suited to imaging other physiologicalfeatures of interest, and to corresponding CAD algorithms.

Existing techniques for computerized diagnosis of physiological featuressuffer from certain drawbacks. For example, the output of the CADanalysis is generally fairly, interactive, requiring assessment andevaluation by a seasoned practitioner. Due to time constraints and theavailability of such persons, a patient is often called upon to reportfor certain types of examination, with further examinations needing tobe scheduled, when appropriate, based upon the review of the CADanalysis. That is to say, patients often must return for additionaltests on the same or a different modality imaging system in order toproperly evaluate and diagnose potential conditions. The resultingprocedure is not only time-consuming for the patient and for thephysician, but ultimately results in the entire process extending over aconsiderable period of time. Additional appointments for subsequentimaging can also result in considerable expense both for the patient,for hospitals and clinics, and for insurance carriers.

For example, thin slice, high-resolution, CT (HRCT) scanner technologygenerates magnitudes of axial and volumetric data that requiressignificant time for radiologists to review. This demanding of more timefrom the radiologist may lessen the number of exams he or she cancomplete on a daily basis. Additionally, the radiologist'sresponsibility for high sensitivity to a vast amount of informationpresented in HRCT images may be threatening and may even discourageradiologists from performing screening (or therapy follow-up) studies inthe first place. An answer to this explosion of data and patientmanagement has been computer-assisted detection (CAD) of features ofinterests (FOIs) within image volumes. As a second-reviewer(complementing the initial radiologist review) CAD provides assistanceto radiologists by setting markers where gray-levels in the CT image areunexpected, match a distinctive pattern, or do not appear as might betypically expected in a healthy individual.

Whether a FOI is detected by a CAD system or by a radiologist (or thecombination of both), the critical step toward informed clinicalmanagement of that feature is in accurate segmentation (from otheranatomic or pathologic structures) and quantification (volumetric,densitometric, functional, geometric, etc.). Since the release ofapplications such as Advanced Lung Analysis (ALA), it has been learnedthat the ability to accurately determine the volumetric size of smallobjects depends on the scan-acquisition and reconstruction variablesused in generating an image volume. Considerable variability insegmentation and sizing of small features may be introduced due topartial-volume effects inherent to multi-slice CT scanner acquisition,patient motion, and mis-registration. Therefore, it is advisable toperform a targeted reconstruction at a small display field of view andoptimal reconstruction parameters to capture maximum detail from adetected FOI. Unfortunately, at the time the radiologist reviews (anddetects) a FOI in a typical screening exam, the raw projection (scan)data has been overwritten or removed from the CT console and thus,retrospective acquisition of projection data is no longer an option.Additionally, sometimes the CAD analysis results in a false positive.

BRIEF DESCRIPTION OF THE INVENTION

In one aspect, a method for acquiring an image on an imaging system isprovided. The method includes accessing at least first image data from afirst imaging system, processing the first image data in accordance witha CAD algorithm, acquiring at least second image data based upon resultsof the CAD algorithm and processing the second image data in accordancewith the CAD algorithm to confirm the results of the CAD algorithmregarding the first image data.

In another aspect, a method for acquiring an image on an imaging systemis provided. The method includes receiving an indication of examinationtype prior to any image data acquisition operation, accessing at leastfirst image data from a first imaging system, processing the first imagedata in accordance with a CAD algorithm, acquiring at least second imagedata based upon results of the CAD algorithm, and post-processing thesecond image data based on the received examination type withoutoperator intervention.

In yet another aspect, a method for acquiring an image on an imagingsystem is provided. The method includes accessing at least first imagedata from a first imaging system using a first scan prescription,processing the first image data in accordance with a CAD algorithm,prompting a user to prescribe a second scan prescription different thanthe first scan prescription based upon results of the CAD algorithm, andacquiring at least second image data using the second scan prescription.

In still another aspect, a method for a seamless display and analysis ofdual resolution image data is provided. The method includes reviewingimage data at low resolution, performing a volumetric analysis of atleast one feature of interest in the low resolution data, substitutinghigh-resolution image data for analyzed low resolution data withoutoperator intervention, and displaying a volume rendering of the lowresolution data and analysis results of the high-resolution data in asingle display.

In one aspect, an imaging system includes a first image data acquisitionsystem configured to acquire medical images, and a computer coupled tothe image data acquisition system and configured to generate a firstseries of images from image data acquired by the acquisition system toprocess series of images via a CAD algorithm, to prescribe acquisitionof a second series of images based upon results of the CAD algorithm,and to process the second series of images via the CAD algorithm toconfirm the result of the CAD algorithm regarding the first series ofimages.

In another aspect, a computer program for acquiring medical image datais provided. The program includes a machine readable medium, and acomputer program stored on the medium and including routines forreceiving an indication of examination type prior to any image dataacquisition operation, acquiring a first series of images from a firstimaging system, processing the first series of images in accordance witha CAD algorithm, acquiring a second series of images based upon resultsof the CAD algorithms, and post-processing the second series of imagesbased on the received examination type without operator intervention.

In still another aspect, a computer program for acquiring medical imagedata is provided. The program including a machine readable medium, and acomputer program stored on the medium and including routines forreceiving low resolution image data, performing a volumetric analysis ofat least one feature of interest in the low resolution data,substituting high-resolution image data for analyzed low resolution datawithout operator intervention, and displaying a volume rendering of thelow resolution data and analysis results of the high-resolution data ina single display.

In yet another aspect, an imaging system includes a first image dataacquisition system configured to acquire medical images, and a computercoupled to the image data acquisition system. The computer is configuredto receive low resolution image data, perform a volumetric analysis ofat least one feature of interest in the low resolution data, substitutehigh-resolution image data for analyzed low resolution data withoutoperator intervention, and display a volume rendering of the lowresolution data and analysis results of the high-resolution data in asingle display.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a pictorial view of a CT imaging system.

FIG. 2 is a block schematic diagram of the system illustrated in FIG. 1.

FIG. 3 is a flow chart illustrating a method to detect suspiciousregions in imaging data.

DETAILED DESCRIPTION OF THE INVENTION

In some known CT imaging system configurations, an x-ray source projectsa fan-shaped beam which is collimated to lie within an X-Y plane of aCartesian coordinate system and generally referred to as an “imagingplane”. The x-ray beam passes through an object being imaged, such as apatient. The beam, after being attenuated by the object, impinges uponan array of radiation detectors. The intensity of the attenuatedradiation beam received at the detector array is dependent upon theattenuation of an x-ray beam by the object. Each detector element of thearray produces a separate electrical signal that is a measurement of thebeam intensity at the detector location. The intensity measurements fromall the detectors are acquired separately to produce a transmissionprofile.

In third generation CT systems, the x-ray source and the detector arrayare rotated with a gantry within the imaging plane and around the objectto be imaged such that the angle at which the x-ray beam intersects theobject constantly changes. A group of x-ray attenuation measurements,i.e., projection data, from the detector array at one gantry angle isreferred to as a “view”. A “scan” of the object comprises a set of viewsmade at different gantry angles, or view angles, during one revolutionof the x-ray source and detector.

In an axial scan, the projection data is processed to construct an imagethat corresponds to a two-dimensional slice taken through the object.One method for reconstructing an image from a set of projection data isreferred to in the art as the filtered backprojection technique. Thisprocess converts the attenuation measurements from a scan into integerscalled “CT numbers” or “Hounsfield units” (HU), which are used tocontrol the brightness of a corresponding pixel on a cathode ray tubedisplay.

To reduce the total scan time, a “helical” scan may be performed. Toperform a “helical” scan, the patient is moved while the data for theprescribed number of slices is acquired. Such a system generates asingle helix from a fan beam helical scan. The helix mapped out by thefan beam yields projection data from which images in each prescribedslice may be reconstructed.

Reconstruction algorithms for helical scanning typically use helicalweighing algorithms that weight the collected data as a function of viewangle and detector channel index. Specifically, prior to a filteredbackprojection process, the data is weighted according to a helicalweighing factor, which is a function of both the gantry angle anddetector angle. The weighted data is then processed to generate CTnumbers and to construct an image that corresponds to a two-dimensionalslice taken through the object.

To further reduce the total acquisition time, multi-slice CT has beenintroduced. In multi-slice CT, multiple rows of projection data areacquired simultaneously at any time instant. When combined with helicalscan mode, the system generates a single helix of cone beam projectiondata. Similar to the single slice helical, weighting scheme, a methodcan be derived to multiply the weight with the projection data prior tothe filtered backprojection algorithm.

As used herein, an element or step recited in the singular and proceededwith the word “a” or “an” should be understood as not excluding pluralsaid elements or steps, unless such exclusion is explicitly recited.Furthermore, references to “one embodiment” of the present invention arenot intended to be interpreted as excluding the existence of additionalembodiments that also incorporate the recited features.

Also as used herein, the phrase “reconstructing an image” is notintended to exclude embodiments of the present invention in which datarepresenting an image is generated but a viewable image is not. However,many embodiments generate (or are configured to generate) at least oneviewable image.

Referring to FIGS. 1 and 2, a multi-slice scanning imaging system, forexample, a Computed Tomography (CT) imaging system 10, is shown asincluding a gantry 12 representative of a “third generation” CT imagingsystem. Gantry 12 has an x-ray tube 14 (also called x-ray source 14herein) that projects a beam of x-rays 16 toward a detector array 18 onthe opposite side of gantry 12. Detector array 18 is formed by aplurality of detector rows (not shown) including a plurality of detectorelements 20 which together sense the projected x-rays that pass throughan object, such as a medical patient 22 between array 18 and source 14.Each detector element 20 produces an electrical signal that representsthe intensity of an impinging x-ray beam and hence can be used toestimate the attenuation of the beam as it passes through object orpatient 22. During a scan to acquire x-ray projection data, gantry 12and the components mounted therein rotate about a center of rotation 24.FIG. 2 shows only a single row of detector elements 20 (i.e., a detectorrow). However, multi-slice detector array 18 includes a plurality ofparallel detector rows of detector elements 20 such that projection datacorresponding to a plurality of quasi-parallel or parallel slices can beacquired simultaneously during a scan.

Rotation of components on gantry 12 and the operation of x-ray source 14are governed by a control mechanism 26 of CT system 10. Controlmechanism 26 includes an x-ray controller 28 that provides power andtiming signals to x-ray source 14 and a gantry motor controller 30 thatcontrols the rotational speed and position of components on gantry 12. Adata acquisition system (DAS) 32 in control mechanism 26 samples analogdata from detector elements 20 and converts the data to digital signalsfor subsequent processing. An image reconstructor 34 receives sampledand digitized x-ray data from DAS 32 and performs high-speed imagereconstruction. The reconstructed image is applied as an input to acomputer 36, which stores the image in a storage device 38. Imagereconstructor 34 can be specialized hardware or computer programsexecuting on computer 36.

Computer 36 also receives commands and scanning parameters from anoperator via a console 40 (operator workstation) that has a keyboard, orother input device. An associated cathode ray tube display 42 allows theoperator to observe the reconstructed image and other data from computer36. The operator supplied commands and parameters are used by computer36 to provide control signals and information to DAS 32, x-raycontroller 28, and gantry motor controller 30. In addition, computer 36operates a table motor controller 44, which controls a motorized table46 to position patient 22 in gantry 12. Particularly, table 46 movesportions of patient 22 through gantry opening 48.

In one embodiment, computer 36 includes a device 50, for example, afloppy disk drive, CD-ROM drive, DVD drive, magnetic optical disk (MOD)device, or any other digital device including a network connectingdevice such as an Ethernet device for reading instructions and/or datafrom a computer-readable medium 52, such as a floppy disk, a CD-ROM, aDVD or another digital source such as a network or the Internet, as wellas yet to be developed digital means. In another embodiment, computer 36executes instructions stored in firmware (not shown). Computer 36 isprogrammed to perform functions described herein, and as used herein,the term computer is not limited to just those integrated circuitsreferred to in the art as computers, but broadly refers to computers,processors, microcontrollers, microcomputers, programmable logiccontrollers, application specific integrated circuits, and otherprogrammable circuits, and these terms are used interchangeably herein.Although the specific embodiment mentioned above refers to a thirdgeneration CT system, the methods described herein equally apply tofourth generation CT systems (stationary detector—rotating x-ray source)and fifth generation CT systems (stationary detector and x-ray source).Additionally, it is contemplated that the benefits of the inventionaccrue to imaging modalities other than CT. Additionally, although theherein described methods and apparatus are described in a medicalsetting, it is contemplated that the benefits of the invention accrue tonon-medical imaging systems such as those systems typically employed inan industrial setting or a transportation setting, such as, for example,but not limited to, a baggage scanning system for an airport or othertransportation center. Additionally, while the herein described methodsand systems refer to human patients, it is contemplated that thebenefits of the invention accrue to systems sized to study animals.

The data collected by DAS 32 may be transmitted to computer 36 andmoreover, to a memory (not shown). It should be understood that any typeof memory to store a large amount of data may be utilized by such anexemplary system 10. Also, computer 36 is configured to receive commandsand scanning parameters from an operator via console 40 typicallyequipped with a keyboard and other input devices. An operator maycontrol the system 10 via the input devices. Thus, the operator mayobserve the reconstructed image and other data relevant to the systemfrom computer 36, initiate imaging, and so forth.

Additionally, the scanned image may also be printed on to a printer (notshown) that may be coupled to computer 36 and operator workstation 40.Further, operator workstation 40 may also be coupled to a picturearchiving and communications system (PACS). It should be noted that thePACS may be coupled to a remote system, a radiology departmentinformation system (RIS), a hospital information system (HIS) or to aninternal or external network, so that others at different locations maygain access to the image and/or to the image data.

It should be further noted that computer 36 and console 40 may becoupled to other output devices, which may include standard, or specialpurpose computer monitors and associated processing circuitry. One ormore operator workstations 40 may be further linked in the system foroutputting system parameters, requesting examinations, viewing images,and so forth. In general, displays, printers, workstations, and similardevices supplied within the system may be local to the data acquisitioncomponents, or may be remote from these components, such as elsewherewithin an institution or hospital, or in an entirely different location,linked to the image acquisition system via one or more configurablenetworks, such as the Internet, virtual private networks, and so forth.

Once reconstructed, the image produced by the system of FIGS. 1 and 2reveals internal features of a patient. In traditional approaches todiagnosis of medical conditions, such as disease states, and moregenerally of medical events, a radiologist or physician would consider ahard copy of display of an image to discern characteristic features ofinterest. Such features might include lesions, sizes and shapes ofparticular anatomies or organs, and other features that would bediscernable in the image based upon the skill and knowledge of theindividual practitioner.

The present technique implements certain of these capabilities by CADalgorithms. As will be appreciated by those skilled in the art, CADalgorithms may offer the potential for identifying, or at leastlocalizing, certain features of interest, such as anatomical anomalies.The particular CAD algorithm is commonly selected based upon the type offeature to be identified, and upon the imaging modality used to createthe image data. The CAD technique may employ segmentation algorithms,which identify the features of interest by reference to known oranticipated image characteristics, such as edges, identifiablestructures, boundaries, changes or transitions in colors or intensities,changes or transitions in spectrographic information, and so forth.Current CAD algorithms generally offer the potential for identifyingthese features only. Subsequent processing and data acquisition is,then, entirely at the discretion and based upon the expertise of thepractitioner.

CAD algorithms may be considered as including several parts or modules,all of which may be implemented in the present technique. In general,the CAD algorithm may include modules such as accessing image data,segmenting data or images, feature selection or extraction,classification, training, and visualization. Moreover, the CADprocessing may be performed on an acquisition projection data set priorto reconstruction, on two-dimensional reconstructed data (both in axialand scout modes), on three-dimensional reconstructed data (volume dataor multiplanar reformats), or a suitable combination of such formats.The acquired projection data set may have a number of one-dimensionalprojections for two-dimensional scans or a number of two-dimensionalprojections for three-dimensional scans. Using the acquired orreconstructed data, segmentation, feature selection, and/orclassification prior to visualization may be performed. These processescan be done in parallel, or in various combinations.

The data on which the CAD algorithm is implemented may be raw imageacquisition system information, or may be partially or completelyprocessed data. The data may originate from a tomographic data source,or may be diagnostic tomographic data (such as raw data in projection orRadon domain in CT imaging, single or multiple reconstructedtwo-dimensional images, or three-dimensional reconstructed volumetricimage data). Because the benefits of the invention accrue to differentdimensional data, the term “area” as used herein refers to both twodimensional areas as well as three dimensional volumes.

The segmentation portion of the CAD algorithm may identify a particularregion of interest based upon calculated features in the tomographicdata. The region of interest can be determined in a number of manners,using an entire data set or using part of a data set, such as acandidate mass region in a specific area. The particular segmentationtechnique may depend upon the anatomies to be identified, and maytypically be based upon iterative thresholding, K-means segmentation,edge detection, edge linking, curve fitting, curve smoothing, two- andthree-dimensional morphological filtering, region growing, fuzzyclustering, image/volume measurements, heuristics, knowledge-basedrules, decision trees, neural networks, and so forth. Alternatively, thesegmentation may be at least partially manual. Automated segmentationmay also use prior knowledge such as shape and size of a mass toautomatically delineate an area of interest.

The feature extraction aspect of the CAD algorithm involves performingcomputations on the data that comprises the desired images. Multiplefeature measures can be extracted from the image-based data using regionof interest statistics, such as shape, size, density, and curvature. Forprojection space data, features such as location, shape, or size offeature projections in a view or location may be used, such as toprovide consistency between views.

The classification aspects of the CAD algorithm may be, again, partiallyor fully manual or automated. In particular, the classification may beused to specifically identify regions of interest, such as byclassification as normal or abnormal anatomies or lesions. Bayesianclassifiers, neural networks, rule-based methods or fuzzy logictechniques, among others, can be used for classification. It should benoted that more than one CAD algorithm could be employed in parallel.Such parallel operation may involve performing CAD operationsindividually on portions of the image data, and combining the results ofall CAD operations (logically by “and”, “or” operations or both). Inaddition, CAD operations to detect multiple disease states or anatomicalfeatures of interest may be performed in series or in parallel.

Prior to classification of masses for anatomies using the CAD algorithm,prior knowledge from training may be incorporated. The training phasemay involve the computation of several candidate features on knownsamples of normal and abnormal lesions or other features of interest. Afeature selection algorithm may then be employed to sort through thecandidate features and select only the useful ones and remove those thatprovide no information, or redundant information. This decision is basedupon classification results with different combinations of candidatefeatures. The feature selection algorithm may also be used to reduce thedimensionality for practical reasons of processing, storage and datatransmission. Thus, optimal discrimination may be performed betweenfeatures or anatomies identified by the CAD algorithm.

The visualization aspect of the CAD algorithm permits reconstruction ofuseful images for review by human or machine observers. Thus, varioustypes of images may be presented to the attending physician or to anyother person needing such information, based upon any or all of theprocessing and modules performed by the CAD algorithm. The visualizationmay include two- or three-dimension renderings, superposition ofmarkers, color or intensity variations, and so forth.

FIG. 3 is a flow chart illustrating a method 100 to detect suspiciousregions of the CT image volume in various types of CT (routine, highresolution, screening, etc.) examinations and automatically perform (1)Retrospective targeted reconstruction at small display field of view andoptimal reconstruction parameters and/or (2) Prospective re-scanning forimproved quantitative image quality in those regions.

In one embodiment, a computer assisted automated targeted reconstructionprospectively creates an alternative reconstruction (high-resolution,small display field of view, alternate reconstruction algorithm,filtering, slice thickness, etc.) of images encompassing the suspectedanatomy or FOI found by a CAD algorithm. These images (alternatereconstructions) are then sent for a radiologist's review within theframework of a normal workflow. These additional images are accessibleto the radiologist in a seamless manner for review by providing thefollowing.

(a) Access to the high resolution images (when available) when theradiologist wants to better visualize using volume rendering withsegmentation, or quantify by determining the volume etc.

(b) An additional source of productivity improvement, wherein thesegmentation and volumetric measurements prescribed prior to the scanand made available for the reader at the time of review. For example,when the user knows what quantitative information will be desired fromthe FOI, that information is requested prior to the scan and the fullprocessing chain is executed with results being available at the time ofreview and will save time from post-processing at the review client. Forexample, computer 36 receives an indication of examination type prior toany image data acquisition operation, accesses at least first image datafrom a first imaging system, processes the first image data inaccordance with a CAD algorithm, acquires at least second image databased upon results of the CAD algorithm, and post-processes the secondimage data based on the received examination type without operatorintervention. Then the post-processed second image, a post-processedfirst image, and the exam type is provided to a reader who may be at theexam site itself or at a remote site.

(c) As a third review opportunity, the retrospectively reconstructedtargeted image series may be re-submitted to the CAD algorithm forfurther review and refinement in order to confirm the detections (i.e.,results of the CAD analysis on the initial scan) based on evaluation ofthe high-resolution data.

(d) The radiologist can also directly view the data with visualizationtechniques other than axial images (for example, 3D reformatted views)at the time of review.

(e) The retrospectively reconstructed images are automatically linkedwith their initial (perhaps thicker slice CT data) image acquisition sothat access to the alternate data types is seamless through theworkflow.

In another embodiment, a radiologist requested targeted reconstructionthat may be needed for better visualization and/or quantification of aFOI that has been identified by the radiologist. This is requested atthe CT console, at a reviewing workstation that is networked to the CTscanner, or at any local or peripheral client or access point to the CTscanner in accordance with the following.

(a) When the radiologist request is made at the CT Console, the requestis serviced immediately from the CT scan data available and this data ismade available through appropriate notification and user interface atthe console.

(b) When the request is made from a networked workstation, this involvescommunicating the request over the network, having the scan dataavailable at the console or recon server, and sending the requestedimages back to the client where the request originated.

(c) The reviewer is provided a notification and a user interface toreceive the images and view them at the review workstation.

In another embodiment, a prospective rescanning of a patient is enabledwherein a computer assisted rescanning of a patient while on the scannerto get high quality images through a FOI improves productivity byobviating the need for a recall of the patient. The prospectiverescanning is, in one embodiment, implemented as follows.

(a) The rescan using an alternative imaging technique and/or acquisitionparameters is triggered by a CAD algorithm that automatically detectsFOIs and prescribes targeted re-scans.

(b) The CAD algorithm may produce notifications to the operator of theCT Console with indication of anatomical FOI's that may need to bescanned at alternate technique.

(c) The operator may interactively acknowledge or reject recommendationsmade by the CAD algorithm for automated re-scan. For example, computer36 accesses at least first image data from a first imaging system usinga first scan prescription, processes the first image data in accordancewith a CAD algorithm, prompts a user to prescribe a second scanprescription different than the first scan prescription based uponresults of the CAD algorithm, and acquires at least second image datausing the second scan prescription. In an exemplary embodiment, computer36 provides recommendations regarding a second scan prescription basedon the results of the CAD algorithm.

Method 100 includes prescribing an initial (first) scan 102, andchecking if a Computer Aided Targeting (CAT) algorithm 104 (including aCAD algorithm) is on. When CAT 104 is on, computer 36 prompts a user toinput a maximum number of hits (Nmax) or to accept a default Nmax. Asused herein a hit refers to instances in which CAD 104 algorithm meetsall criteria for what the CAD algorithm is looking for. Computer 36 thenprompts the user to confirm 106 the received scan parameters. When CAT104 is active, the prescribed scan is performed and the obtained data issubmitted 108 to CAT 104. When CAT 104 is not active, the obtained datais saved 110. When N hits are generated 112 from CAT 104, differentcourses of action are performed dependent upon a comparison 114 of N toNmax. When N is not greater than Nmax, the targeted data 116 (i.e., theoriginal obtained data targeted by CAT 104) is used to reconstructtargeted images which are saved 118. When N is greater than Nmax, theuser is informed of this and prompted with the choice of stopping anddoing nothing, reconstructing all hits (i.e., reconstructing a pluralityof targeted images based upon the CAT), and importing the targeted datainto another application 120 such as a quantification application suchas for example an Advanced Lung Analysis (ALA) program.

Technical effects of the herein described methods and apparatus includethe potential for further enhancing the automation offered by CADtechniques by enabling either further processing image data or furtheracquisition of image data. In the case of processing, various parametersemployed in post-processing of the acquired image data may be altered soas to render the reconstructed image more revealing or useful inidentifying, localizing, and/or diagnosing a physiological condition. Inparticular, such parameters may include contrast, spatial resolution(e.g. zoom), color, and so forth. Moreover, the post-processing basedupon the results of initial CAD evaluation may include mathematicalevaluations such as segmentation, registration, computation of areas orvolumes, and so forth. The “post-processing” may also involve the use ofdifferent reconstruction algorithms or different reconstructionparameters to generate images. For example, based on initial CADresults, different filter kernels (Soft, Standard, Detail, Bone, Edge,Lung, etc.) may be used to produce additional images from the originalscan. Different filter kernels enhance different desired features in theimage. Other reconstruction parameters, such as reconstructionfield-of-view, matrix size, targeting locations, etc. can also bemodified to produce additional images based on the initial CAD results.

The initial CAD evaluation may also enable the automatic acquisition ofsubsequent images so as to enable a complete useful set of informationto be gathered during a single patient session. The subsequentprocessing may be in order due, for example, to particular features thatappear in images initially acquired but which are not adequately shown.Thus, the subsequent acquisition may include acquisition of data fromother regions of the patient's body, at different orientations withrespect to tissues of interest, at different resolution levels, and soforth. Moreover, entirely different acquired data may be desired basedupon the initial CAD evaluation, such as data acquired via an entirelydifferent modality system.

It should be noted that, as mentioned above, while initial images may bereconstructed and the CAD algorithm applied to the image data asdescribed herein, the analysis may be partially or fully performedwithout such initial visualization. Thus, in case of CT image data, someor all of the CAD algorithm analysis may take place in Radon space.Ultimate useful image reconstruction may include visualizations ofinitial images, enhanced images, or both. The results of the CADanalysis may, where desired, even guide the type of image reconstructionperformed, such as from Radon space in the CT imaging example.

By way of example, image data may be acquired from an X-ray system andthe image data analyzed to identify a feature of potential interest.Images may be reconstructed based on the X-ray image data. Subsequentimage acquisition may then be ordered via a CT system to provide abetter view of the particular identified feature. One or more images maythen be reconstructed based on the CT image data. As noted above, theactual image reconstruction based on the initial data may be optional,or at least distinct from the analysis performed by the CAD algorithmand the subsequent acquisition of the second image data.

Exemplary embodiments are described above in detail. The methods andapparatus are not limited to the specific embodiments described herein,but rather, components of each method and/or apparatus may be utilizedindependently and separately from other components described herein.

While the invention has been described in terms of various specificembodiments, those skilled in the art will recognize that the inventioncan be practiced with modification within the spirit and scope of theclaims.

1. A method for acquiring an image on an imaging system, said methodcomprising: accessing at least first image data from a first imagingsystem; processing the first image data in accordance with a CADalgorithm; acquiring at least second image data based upon results ofthe CAD algorithm; and processing the second image data in accordancewith the CAD algorithm to confirm the results of the CAD algorithmregarding the first image data.
 2. A method in accordance with claim 1,wherein the second image data is acquired from the first imaging system.3. A method in accordance with claim 1, wherein the second image data isacquired from a second imaging system.
 4. A method in accordance withclaim 3, wherein the first and second imaging systems are of differentimaging modalities.
 5. A method in accordance with claim 3, wherein atleast one of the first and second imaging systems is a CT system.
 6. Amethod in accordance with claim 1, wherein the first imaging system is aCT system.
 7. A method in accordance with claim 1, wherein the secondimage data is acquired on the first imaging system but with a differentsystem configuration than that used for acquiring the first image data.8. A method for acquiring an image on an imaging system, said methodcomprising: receiving an indication of examination type prior to anyimage data acquisition operation; accessing at least first image datafrom a first imaging system; processing the first image data inaccordance with a CAD algorithm; acquiring at least second image databased upon results of the CAD algorithm; and post-processing the secondimage data based on the received examination type without operatorintervention.
 9. A method in accordance with claim 8, further comprisingdisplaying the post-processed second image data without operatorintervention.
 10. A method in accordance with claim 8, wherein thesecond image data is acquired without operator intervention.
 11. Amethod in accordance with claim 8, further comprising prompting a userto prescribe a scan prescription different than a scan prescription usedto generate the first image data based upon results of the CADalgorithm.
 12. A method in accordance with claim 11, wherein saidprompting comprises prompting the operator to use an imaging modalitydifferent than a modality used to generate the first image data.
 13. Amethod in accordance with claim 11, wherein said prompting comprisesprompting the operator to use an imaging modality the same as a modalityused to generate the first image data but with different parameters. 14.A method in accordance with claim 11, wherein said prompting comprisesrecommending a scan prescription based upon results of the CADalgorithm.
 15. A method in accordance with claim 12, wherein themodality used to generate the first image data is CT.
 16. A method foracquiring an image on an imaging system, said method comprising:accessing at least first image data from a first imaging system using afirst scan prescription; processing the first image data in accordancewith a CAD algorithm; prompting a user to prescribe a second scanprescription different than the first scan prescription based uponresults of the CAD algorithm; and acquiring at least second image datausing the second scan prescription.
 17. A method in accordance withclaim 16, wherein said prompting comprises recommending a scanprescription based upon results of the CAD algorithm.
 18. A method inaccordance with claim 17, wherein said recommending comprisesrecommending an imaging modality different than an imaging modality usedto obtain the first image data.
 19. A method in accordance with claim18, wherein the modality used to obtain the first image data is CT. 20.A method in accordance with claim 17, wherein said recommendingcomprises recommending an imaging modality the same as an imagingmodality used to obtain the first image data.
 21. A method in accordancewith claim 16, further comprising processing the second image data inaccordance with the CAD algorithm to confirm the result of the CADalgorithm regarding the first image data.
 22. A method for seamless adisplay and analysis of dual resolution image data, said methodcomprising: reviewing image data at low resolution; performing avolumetric analysis of at least one feature of interest in the lowresolution data; substituting high-resolution image data for analyzedlow resolution data without operator intervention; and displaying avolume rendering of the low resolution data and analysis results of thehigh-resolution data in a single display.
 23. A method in accordancewith claim 22 wherein an area in an object in which the high-resolutiondata represents is selected based on results of a CAD algorithm.
 24. Amethod in accordance with claim 22 wherein the high resolution data ispresent for only the features of interest identified by a CAD algorithm.25. A method in accordance with claim 22 further comprising obtaininghigh resolution data representative of an area in an object for whichhigh resolution data is absent.
 26. An imaging system comprising: afirst image data acquisition system configured to acquire medicalimages; and a computer coupled to the image data acquisition system andconfigured to generate a first series of images from image data acquiredby the acquisition system to process series of images via a CADalgorithm, to prescribe acquisition of a second series of images basedupon results of the CAD algorithm, and to process the second series ofimages via the CAD algorithm to confirm the result of the CAD algorithmregarding the first series of images.
 27. A system in accordance withclaim 26, wherein the second series of images is acquired from the firstimage data acquisition system.
 28. A system in accordance with claim 26,further comprising a second image data acquisition system, wherein thesecond series of images is acquired from the second image dataacquisition system.
 29. A system in accordance with claim 28, whereinthe first and second image data acquisition systems are of differentimaging modalities.
 30. A system in accordance with claim 26, whereinsaid computer further configured to receive an indication of exam typeprior to any image acquisition operation, and post-process the secondseries of images based on the received examination type without operatorintervention.
 31. A system in accordance with claim 26 wherein the firstimage data acquisition system is a CT system.
 32. A system in accordancewith claim 26, wherein the second series of images is acquired on thefirst image data acquisition system but with a different imageconfiguration than that used for acquiring the first series of images.33. A system in accordance with claim 26, wherein the second series ofimages is acquired without operator intervention.
 34. A system inaccordance with claim 26, wherein the computer system is configured topropose the prescribed acquisition of the second series of images in anoperator interface of the first image data acquisition system.
 35. Acomputer program for acquiring medical image data, the programcomprising: a machine readable medium; and a computer program stored onthe medium and including routines for receiving an indication ofexamination type prior to any image data acquisition operation,acquiring a first series of images from a first imaging system;processing the first series of images in accordance with a CADalgorithm; acquiring a second series of images based upon results of theCAD algorithms, and post-processing the second series of images based onthe received examination type without operator intervention.
 36. Aprogram in accordance with claim 35, wherein the computer programfurther includes a routine to confirm the result of the CAD algorithmregarding the first image data.
 37. A program in accordance with claim35, wherein the second series of images is acquired from a secondimaging system.
 38. A program in accordance with claim 37, wherein thefirst and second imaging systems are of different imaging modalities.39. A program in accordance with claim 36, wherein the computer programfurther includes routines for prompting a user to prescribe a secondscan prescription different than the first scan prescription based uponresults of the CAD algorithm, and acquiring at least second image datausing the second scan prescription.
 40. A computer program for acquiringmedical image data, the program comprising: a machine readable medium;and a computer program stored on the medium and including routines for:receiving low resolution image data; performing a volumetric analysis ofat least one feature of interest in the low resolution data;substituting high-resolution image data for analyzed low resolution datawithout operator intervention; and displaying a volume rendering of thelow resolution data and analysis results of the high-resolution data ina single display.
 41. A program in accordance with claim 40 wherein anarea in an object in which the high-resolution data represents isselected based on results of a CAD algorithm.
 42. A program inaccordance with claim 40 wherein the high resolution data is present foronly the features of interest identified by a CAD algorithm.
 43. Aprogram in accordance with claim 40 further comprising a routine forobtaining high resolution data representative of an area in an objectfor which high resolution data is absent.
 44. An imaging systemcomprising: a first image data acquisition system configured to acquiremedical images; and a computer coupled to the image data acquisitionsystem and configured to: receive low resolution image data; perform avolumetric analysis of at least one feature of interest in the lowresolution data; substitute high-resolution image data for analyzed lowresolution data without operator intervention; and display a volumerendering of the low resolution data and analysis results of thehigh-resolution data in a single display.
 45. A system in accordancewith claim 44 wherein an area in an object in which the high-resolutiondata represents is selected based on results of a CAD algorithm.
 46. Asystem in accordance with claim 44 wherein the high resolution data ispresent for only the features of interest identified by a CAD algorithm.47. A system in accordance with claim 44 further comprising a routinefor obtaining high resolution data representative of an area in anobject for which high resolution data is absent.